In a radio communication field of recent years, MIMO (Multiple Input Multiple Output) transmission which is capable of high speed transmission without widening a frequency bandwidth, using a plurality of transmit-receive antennas, is adopted in many systems. Generally, in the MIMO transmission, in order to transmit a plurality of data streams using the same frequency, MIMO signal detection is needed in a reception device.
Among MIMO signal detection technologies, there is maximum likelihood detection (MLD) as an optimal detection technology. The MLD is a technology of detecting a transmit signal candidate of which a likelihood function is at the maximum, among all transmit signal candidates. Since the transmit signal candidates exponentially increase in accordance with the number of constellations or the number of transmission streams of a modulation scheme, the MLD has a problem that a calculation amount becomes very large.
In NPL 1, it is disclosed a technology of reducing the calculation amount of the MLD by reducing the transmit signal candidates with the low calculation amount while lessening performance degradation from the MLD, and by multidimensionally searching for noise enhancement which is caused by MMSE in a case of making MMSE (Minimum Mean Square Error) detection which is a linear detection scheme as a reference point. A method for generating the transmit signal candidate in NPL 1, will be described.
An NR dimension received signal vector y is represented as follows. Furthermore, NR represents the number of receive antennas. Moreover, the number of transmit antennas is represented by NT.[Math. 1]y=Hs=n  (1)
Here, H represents a channel matrix of an NR row and an NT column, and s represents a transmit signal vector of an NT dimension, and n represents a noise vector of the NR dimension.
An MMSE detection result x^ is represented as follows.[Math. 2]{circumflex over (x)}=PHHy  (2)P=(HHH+σn2INT)−1  (3)
Here, σn2 represents noise electric power, and INT represents an identity matrix of an NT row and an NT column. Moreover, superscript H represents a complex conjugate transpose matrix.
In NPL 1, the transmit signal candidate is generated by quantizing the following s^ using the MMSE detection result x^.
      [          Math      .                          ⁢      3        ]                                            s            ~                    =                                    x              ^                        +                                          ∑                                  k                  =                  1                                                  N                  P                                            ⁢                                                          ⁢                                                a                  k                                ⁢                                  λ                  k                                      1                    ⁢                                          /                                        ⁢                    2                                                  ⁢                                  v                  k                                                                                          (          4          )                    
NP is 1≦NP≦NT, and ak is calculated as follows.[Math. 4]a=e(m·k)({tilde over (c)}kH{tilde over (c)}k)−1{tilde over (c)}k  (5)aH=[a1*,a2*, . . . , aNP*]  (6)e(m,k)=b(m)−({circumflex over (x)})k  (7)
Furthermore, b(m) is one of the constellations of the modulation scheme, and is 1≦m≦M. M is the number of constellations, and for example, if the modulation scheme is QPSK (Quadrature Phase Shirt Keying), M=4, and if the modulation scheme is 16QAM (Quadrature Amplitude Modulation), M=16. Moreover, (·)k represents a k-th element of the vector.
Additionally, c˜k is represented as follows.[Math. 5]{tilde over (c)}kH=[λ11/2(v1)k,λ21/2(v2)k, . . . , λNP1/2(vNP)k]  (8)
λ1 to λNP, and v1 to vNP represent an eigenvalue which is obtained by eigenvalue decomposition of P, and an eigenvector thereof, respectively.[Math. 6]P=VDVH  (9)V=[v1,v2, . . . , vNT]  (10)D=diag[λ1,λ2, . . . , λNT]  (11)
Furthermore, diag[·] represents a diagonal matrix.